Definition and Essential Features

The First International Meeting on Brain Computer Interface Technology (2000) defined a brain-computer interface (BCI) as " a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles [70]." Essentially, a BCI is a machine that can decode human intent from brain signal alone to create a new communication channel for patients with severe motor impairments [71]. A real-world example of this would entail a subject "locked in" by a brainstem stroke controlling a cursor on a screen with his/her ECoG signal alone. The construct would not require the assistance of overt motor activity. It is important to underline this point. A true BCI allows for a completely novel output pathway from the brain. Wolpaw, in a review of BCI technology, states this principle cogently [71]:

A BCI changes electrophysiological signals from mere reflections of central nervous system (CNS) activity into the intended products of that activity: messages and commands that act on the world. It changes a signal such as an EEG rhythm or a neuronal firing rate from a reflection of brain function into the end product of that function: an output that, like output in conventional neuro-muscular channels, accomplishes the person's intent. A BCI replaces nerves and muscles and the movements they produce with electrophysiological signals and the hardware and software that translate those signals into actions.

Given this new output channel, the patient using the BCI must have feedback to improve the performance of how they alter their brain signals. Just as a child learns to catch a ball, or an athlete perfecting certain moves, there must be continuous alteration of the subject's neuronal output (whether neuro-muscular or electrophysiological) matched against feedback from their overt actions such that the subject's output can be tuned to optimize their performance toward the intended goal. Therefore, the brain must adapt its signals to improve performance. The BCI, additionally, should also be able to evolve to the changing milieu of the user's brain to further optimized functioning. This dual adaptation requires a certain level of training and learning curve, both for the user and the computer. The better the computer and subject are able to adapt, the shorter the training required for control.

There are four essential elements to the practical functioning of a brain-computer interface platform. (Figure 12.1) [71]:

1. Signal acquisition: the BCI system's recorded brain signal or information input.

2. Signal processing: the conversion of raw signal information into a useful device command.

4. Device output: the overt command or control functions that are administered by the BCI system.

4. Operating protocol: the manner in which the system is turned on and off.

All four elements act in concert to actualize the user's intention to his/her surroundings.

Signal acquisition is a measurement of the electrophysiological state of the brain in realtime. This measure of brain activity is usually recorded with electrodes. This is, however, not a theoretical requirement. These electrodes can be either invasive (implanted beneath the skin) or noninvasive (signal acquired externally). The most common types of signals include electroencephalography (EEG), electrical brain activity recorded from the scalp [15, 16, 19, 46, 62, 67], electrocorticography (ECoG) [32, 34], electrical brain activity recorded beneath the skull [32, 34, 54], field potentials, measured by electrodes monitoring brain activity from within the parenchyma [1], and "single units," activity

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